Audio Bandwidth Research Articles

Analysis of the application of college popular music education relying on the elite teaching optimization algorithm

ABSTRACT This paper presents a novel approach to enhancing college popular music education by incorporating an elite teaching optimization algorithm into a customized model. A key feature of the proposed model is the utilization of a high-fidelity wideband audio coding algorithm, which employs a time-frequency analysis module based on the MDCT fast algorithm to reduce storage requirements. The psychoacoustic analysis module takes advantage of the high frequency domain resolution of FFT and accounts for differences between MDCT and DFT by calculating masking curves separately. The proposed model is evaluated using the elite teaching optimization algorithm, and results demonstrate that it effectively improves the effectiveness of college popular music education.

Optimization of Slew Mitigation Capacitor in Passive Charge Compensation-Based Delta–Sigma Modulator

In this paper we propose a scheme of optimizing the size of charge compensation (CC) capacitor in a delta-sigma modulator (DSM) using a passive charge compensation (PCC) based switched capacitor integrator (SCI). The slewing behavior of a PCC based SCI is analyzed in both integration phase (IP) and sampling phase (SP) to optimize the size of CC capacitor. The effectiveness of the proposed scheme is demonstrated by implementing a 2-1 cascaded DSM using PCC based SCI with optimized value of CC capacitor in 0.18-μm CMOS technology. The DSM operates at a frequency of 5-MHz and achieves a peak SNDR of 103.1-dB in the audio bandwidth of 20-kHz. The power consumption of DSM is 220-μW at a supply voltage of 0.85-V and it consumes 1.3 mm of area. Post-layout simulations show an improvement of 10.8-dB in the SNDR of DSM by using the optimized value of CC capacitor in PCC based SCI.

BEHM-GAN: Bandwidth Extension of Historical Music Using Generative Adversarial Networks

Audio bandwidth extension aims to expand the spectrum of bandlimited audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes a method for the bandwidth extension of historical music using generative adversarial networks (BEHM-GAN) as a practical solution to this problem. The proposed method works with the complex spectrogram representation of audio and, thanks to a dedicated regularization strategy, can effectively extend the bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is designed to be applied as a second step after denoising the recording to suppress any additive disturbances, such as clicks and background noise. We train and evaluate the method using solo piano classical music. The proposed method outperforms the compared baselines in both objective and subjective experiments. The results of a formal blind listening test show that BEHM-GAN significantly increases the perceptual sound quality in early-20th-century gramophone recordings. For several items, there is a substantial improvement in the mean opinion score after enhancing historical recordings with the proposed bandwidth-extension algorithm. This study represents a relevant step toward data-driven music restoration in real-world scenarios.

Forensic authentication method for audio recordings generated by Voice Recorder application on Samsung Galaxy Watch4 series.

The number of smartwatch users has been rapidly increasing in recent years. A smartwatch is a wearable device that collects various types of data using sensors and provides basic functions, such as healthcare-related measurements and audio recording. In this study, we proposed the forensic authentication method for audio recordings from the Voice Recording application in the Samsung Galaxy Watch4 series. First, a total of 240 audio recordings from each of the four different models, paired with four different smartphones for synchronization via Bluetooth, were collected and verified. To analyze the characteristics of smartwatch audio recordings, we examined the transition of the audio latency, writable audio bandwidth, timestamps, and file structure between those generated in the smartwatches and those edited using the Voice Recording application of the paired smartphones. In addition, the devices with the audio recordings were examined via the Android Debug Bridge (ADB) tool and compared with the timestamps stored in the file system. The experimental results showed that the audio latency, writable audio bandwidth, and file structure of audio recordings generated by smartwatches differed from those generated by smartphones. Additionally, by analyzing the file structure, audio recordings can be classified as unmanipulated, manipulation has been attempted, or manipulated. Finally, we can forensically authenticate the audio recordings generated by the Voice Recorder application in the Samsung Galaxy Watch4 series by accessing the smartwatches and analyzing the timestamps related to the audio recordings in the file system.

Birdsong

Have your ever felt as happy as a lark, feathered your nest or taken someone under your wing? As we watch birds, we cannot help but be struck by their uncannily familiar behaviors - singing, nest building, caring for their young - to name just a few. Songbirds - the oscine suborder of perching birds that constitute roughly half (∼4,000) of all known avian species - are noted for the songs that males and sometimes both sexes in this group sing to court mates and defend territory from rivals. Birdsongs contain several to many acoustically distinct syllables, typically organized into a stereotyped phrase, and span the same audio bandwidth that we exploit for speech and music, making them easy for us to hear and appreciate. Consequently, eavesdropping humans long ago detected the most striking parallel between songbirds and humans: juvenile songbirds learn to sing in a manner similar to a child learning to speak.

Aerosense: Long-Range Bluetooth Wireless Sensor Node for Aerodynamic Monitoring on Wind Turbine Blades

Predictive maintenance and structural health monitoring are challenging and promising research fields today. In particular, cost-effective and long-term monitoring of wind turbines has been proven to be one of the key elements to successfully increase their efficiency. Accurate numerical modeling and real-time control-in-the-loop play an increasingly prominent role in understanding and optimizing blade aerodynamic and acoustic performances. A non-intrusive and modular measurement system is a prerequisite for long-term measurement campaigns in existing and future wind turbines. Current methods of performing aerodynamic and acoustic field measurements are cumbersome and expensive, leading to a shortage of aerodynamic and acoustic datasets on operating wind turbines. This paper demonstrates the ability of the new Aerosense system to operate successfully in the field. Aerosense is a long-lasting battery-operated and flexible wireless sensor node that can directly measure aerodynamic and acoustic effects on wind turbine blades. It consists of an array of state-of-the-art Micro-Electro-Mechanical Systems (MEMS) sensors, including 40 barometers and 10 microphones, combined with an ultra low power system-on-chip with wireless transmission over Bluetooth 5.1. Experimental results demonstrate the possibility of continuously acquiring data for up to four months on a single lithium battery of 8.7 Ah, featuring an absolute accuracy of 10Pa and an audio bandwidth of 6kHz.

Automatic discrimination between front and back ensemble locations in HRTF-convolved binaural recordings of music

One of the greatest challenges in the development of binaural machine audition systems is the disambiguation between front and back audio sources, particularly in complex spatial audio scenes. The goal of this work was to develop a method for discriminating between front and back located ensembles in binaural recordings of music. To this end, 22, 496 binaural excerpts, representing either front or back located ensembles, were synthesized by convolving multi-track music recordings with 74 sets of head-related transfer functions (HRTF). The discrimination method was developed based on the traditional approach, involving hand-engineering of features, as well as using a deep learning technique incorporating the convolutional neural network (CNN). According to the results obtained under HRTF-dependent test conditions, CNN showed a very high discrimination accuracy (99.4%), slightly outperforming the traditional method. However, under the HRTF-independent test scenario, CNN performed worse than the traditional algorithm, highlighting the importance of testing the algorithms under HRTF-independent conditions and indicating that the traditional method might be more generalizable than CNN. A minimum of 20 HRTFs are required to achieve a satisfactory generalization performance for the traditional algorithm and 30 HRTFs for CNN. The minimum duration of audio excerpts required by both the traditional and CNN-based methods was assessed as 3 s. Feature importance analysis, based on a gradient attribution mapping technique, revealed that for both the traditional and the deep learning methods, a frequency band between 5 and 6 kHz is particularly important in terms of the discrimination between front and back ensemble locations. Linear-frequency cepstral coefficients, interaural level differences, and audio bandwidth were identified as the key descriptors facilitating the discrimination process using the traditional approach.

КОДИРОВАНИЕ ИЗОБРАЖЕНИЙ СПЕКТРОГРАММ ДЛЯ ОБЕСПЕЧЕНИЯ ПЕРЕМЕННОЙ СКОРОСТИ ПЕРЕДАЧИ АУДИОДАННЫХ С СОХРАНЕНИЕМ КАЧЕСТВА ИХ ЗВУЧАНИЯ

In the applications of audio control and fixation in the conditions of information-technical counteraction, noise clearing, formation of digital watermarks, audio fingerprinting, protective text audio markers, etc., a compact representation of speech signals for subsequent transmission-storage is required while maximal preserving the similarity of the sound quality of restored speech with the original, elimination of accompanying interferences. Theproposed audio codec is based on the narrow-band sine Gaussian model of speech analysis/synthesis, where its representation as a superposition of harmonic components weighted by a Gaussian window applies to all types of speech frames, as well as on universal and special methods of construction and image processing of narrow-band dynamic spectrograms, in particular, by the application of compression-recovery algorithms to them, which will allow to regulate the speech stream speed within a wide range of 1.2–16Kbit/s with adaptation to changes of the audio data transmission-storage channel bandwidth, caused, in particular, by both objective factors and the actions of an intruder. This work aims to select the best parameters on the spectrogram images that reduce the overall bitrate, remove the influence of noise and interference and allow using of spectral inversion methods and algorithms to recover the speech signal with the same or better quality. The parameters are extracted from the spectrogram images obtained using of the short-time Fourier transform, using methods to extract the amplitudes, frequencies, phases and development tracks of selected local or global maxima (peaks) of the speech signal on the spectral slices. The communication channel can transmit either the parameters themselves, or the results of compression-encoding of the image to restore the image of the original spectrogram with the selection of peak parameters already on it with the subsequent synthesis of speech or for direct spectral inversion of the image into speech. It is possible to correct the reconstructed spectrogram by using a priori information about the speaker's speech from his pre-generated voice database.

Sonic Cyberfeminisms, Perceptual Coding and Phonographic Compression

I argue that sound-centric scholarship can be of use to feminist theorists if and only if it begins from a non-ideal theory of sound; this article develops such a theory. To do this, I first develop more fully my claim that perceptual coding was a good metaphor for the ways that neoliberal market logics (re)produce relations of domination and subordination, such as white supremacist patriarchy. Because it was developed to facilitate the enclosure of the audio bandwidth, perceptual coding is especially helpful in centring the ways that patriarchal racial capitalism structures our concepts and experiences of both sound and technology. The first section identifies sonic cyberfeminist practices that function as a kind of perceptual coding because they subject ‘sound’ and/or ‘women’ to enclosure and accumulation by dispossession. The second section identifies a type of sonic cyberfeminism that tunes into the parts of the spectrum that this perceptual coding discards, building models of community and aesthetic value that do not rely on the exclusion of women, especially black women, from both humanist and posthuman concepts of personhood. Here I focus especially on Alexander Weheliye’s ‘phonographic’ approach to sound, technology and theoretical text. This approach, which he develops in his 2005 book of that title and in recent work in collaboration with Katherine McKittrick, avoids fetishising tech and self-transformation and focuses on practices that build registers of existence that hegemonic institutions perceptually code out of circulation. I conclude with examples of such phonographic compression, including Masters At Work’s ballroom classic ‘The Ha Dance’ and Nicki Minaj’s ‘Anaconda’.

On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks

In this paper, we address a sub-topic of the broad domain of audio enhancement, namely musical audio bandwidth extension. We formulate the bandwidth extension problem using deep neural networks, where a band-limited signal is provided as input to the network, with the goal of reconstructing a full-bandwidth output. Our main contribution centers on the impact of the choice of low pass filter when training and subsequently testing the network. For two different state of the art deep architectures, ResNet and U-Net, we demonstrate that when the training and testing filters are matched, improvements in signal-to-noise ratio (SNR) of up to 7dB can be obtained. However, when these filters differ, the improvement falls considerably and under some training conditions results in a lower SNR than the band-limited input. To circumvent this apparent overfitting to filter shape, we propose a data augmentation strategy which utilizes multiple low pass filters during training and leads to improved generalization to unseen filtering conditions at test time.

Artificial bandwidth extension of speech signals

Artificial Bandwidth Extension (ABWE) algorithms try to reconstruct frequency components of speech signals which have been lost by coding/transmission. From a system theoretical point of view, this seems to be questionable. However, by exploiting redundancy of speech production as well as psychoacoustic properties of auditory perception, missing parts can subjectively be recovered or at least mimicked to a certain extent. The motivation for ABWE is to bridge the quality gap between the traditional narrowband telephony (200–3400 Hz) and the recent wideband services (HD voice, 50–7000 Hz). An HD terminal which is connected to a traditional phone, may activate ABWE for improving the audio quality and possibly the intelligibility of the received signal. In this contribution, state-of-the-art ABWE algorithms and coding standards including ABWE techniques are reviewed. ABWE algorithms are based on a source-filter model. The three main sub-tasks are to estimate the excitation signal, the spectral envelope and the time domain envelope, given either just the narrowband signal or in addition a few bits of side information. Furthermore, insights with respect to the relative importance of these three components are presented.

AN EFFICIENT SPEECH GENERATIVE MODEL BASED ON DETERMINISTIC/STOCHASTIC SEPARATION OF SPECTRAL ENVELOPES

The paper presents a speech generative model that provides an efficient way of generating speech waveform from its amplitude spectral envelopes. The model is based on hybrid speech representation that includes deterministic (harmonic) and stochastic (noise) components. The main idea behind the approach originates from the fact that speech signal has a determined spectral structure that is statistically bound with deterministic/stochastic energy distribution in the spectrum. The performance of the model is evaluated using an experimental low-bitrate wide-band speech coder. The quality of reconstructed speech is evaluated using objective and subjective methods. Two objective quality characteristics were calculated: Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ). Narrow-band and wide-band versions of the proposed solution were compared with MELP (Mixed Excitation Linear Prediction) speech coder and AMR (Adaptive Multi-Rate) speech coder, respectively. The speech base of two female and two male speakers were used for testing. The performed tests show that overall performance of the proposed approach is speaker-dependent and it is better for male voices. Supposedly, this difference indicates the influence of pitch highness on separation accuracy. In that way, using the proposed approach in experimental speech compression system provides decent MBSD values and comparable PESQ values with AMR speech coder at 6,6 kbit/s. Additional subjective listening testsdemonstrate that the implemented coding system retains phonetic content and speaker’s identity. It proves consistency of the proposed approach.

Self-Tuned Class-D Audio Amplifier With Post-Filter Digital Feedback Implemented on Digital Signal Controller

This article describes the designing process of a low-cost class-D audio amplifier with digitized global feedback after the output filter, using a signal microcontroller. Various approaches to the feedback-based distortions elimination of class-D amplifiers are discussed. Hardware architecture of the author’s designed class-D amplifier with digital audio input interface, output voltage feedback path relays on SAR-ADC, and output power stage in multiphase bridge tide load configuration is shown. The selection method of the output low-pass filter parameters and power stage phase count was explained. A model of class-D amplifier with PID digital compensator and control loop interfaces is presented. Efficient high-speed digital PID compensator implementation on fixed-point 16-bit DSP core is exhibited. Procedure for control loop gain and phase plots measurements and self-tuning of PID feeding under damped second-order plants is presented along with simulations of loop properties influences on tuning effects. The results of the prototype device measurements such as Bode plots or output voltage frequency spectrum are presented. A big effort has been put on a useful, complete and transparent description for the development process of the presented class-D amplifier arrangement. The described class-D amplifier is the first solution with single feedback control from the output of LC filter based on a microcontroller with embedded ADC and PWM hardware with ensuring full audio bandwidth reproduction width THD distortion level below 0.25% at 1 kHz. The amplifier has been designed with the solicitude of device cost by using low-volume inductors in the output filter and by utilization of the built-in microcontroller ADC and PWM interfaces.

A Discrete-Time Audio $\Delta\Sigma$ Modulator Using Dynamic Amplifier With Speed Enhancement and Flicker Noise Reduction Techniques

This article presents a discrete-time second-order $\Delta \Sigma $ modulator for the audio applications. In this modulator, a novel dynamic amplifier is proposed to realize the switched-capacitor (SC) integrators. To eliminate the common-mode (CM) voltage drop in a closed-loop dynamic amplifier during the integration phase, without the use of additional load capacitance, the reset method for the amplifier is modified. Two auxiliary branches are introduced to enhance the settling speed of the integrator. Two different flicker noise reduction techniques (FNRTs) are developed to improve the signal-to-noise-and-distortion ratio (SNDR) (about 2 dB in the audio bandwidth). The prototype modulator is fabricated in 65-nm CMOS technology with a 0.12-mm2 core area, which achieves a dynamic range (DR) of 91 dB and a peak SNDR of 89.6 dB in the 24-kHz signal bandwidth. It consumes only 49- $\mu \text{W}$ power from a 0.8-V supply, translating into a Schreier figure of merit (FoM) of 176.5 dB.

Practical open-concentric spherical microphone array design

The problem of higher order sound field capture is considered. First-order microphone arrays, such as tetrahedral A-format designs, are commonplace, while interest remains in the increased spatial resolution delivered by higher order arrays. Such arrays typically consist of pressure microphones mounted on a solid spherical baffle, with which higher-order spatial components are estimated algorithmically. This produces a design trade-off, with small arrays being preferred for spatial aliasing performance and large arrays being preferred for reduced amplification of microphone capsule noise at low frequencies. A practical open sphere design is proposed that contains microphones mounted at multiple radii to fulfill both criteria. Coupled with the use of cardioid microphones, such an array captures higher order soundfields over a wider audio bandwidth than baffled spherical designs of fixed radius.

A new source‐filter model audio bandwidth extension using high frequency perception feature for IoT communications

SummaryAudio coding is a generic technology to IoT applications system. Audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, high frequency signal is generated by a duplication of the corresponding low frequency and some parameters of high frequency. However, the perception quality of coding will significantly degrade if the correlation between high and low frequency becomes weak. In this paper, we proposed a new source‐filter model audio bandwidth extension method. In our method, a perception feature of the high frequency signal is extracted to restore the perception quality of coding. In the decoder side, the crest factor and noise level are obtained under the constraint of the high frequency perception parameter. The performance shown in our experiment results are superior to the classic methods. Compared with the state of art method, the proposed method is also comparative because of the coding bitrate is significantly reduced and keeps a close perception quality of coding. This paper also provided a new solution for IoT communications that requires low bitrates and high quality of coding, especially like Internet of Vehicles.

Analysis of pulmonary sounds for the diagnosis of interstitial lung diseases secondary to rheumatoid arthritis

The diagnosis of interstitial lung diseases in patients affected by rheumatoid arthritis is fundamental to improving their survival rate. In particular, the average survival time of patients affected by rheumatoid arthritis with pulmonary implications is approximately 3 years. The gold standard for confirming the diagnosis of this disease is computer tomography. However, it is very difficult to raise diagnosis suspicion because the symptoms of the disease are extremely common in elderly people. The detection of the so-called velcro crackle in lung sounds can effectively raise the suspicion of an interstitial disease and speed up diagnosis. However, this task largely relies on the experience of physicians and has not yet been standardized in clinical practice. The diagnosis of interstitial lung diseases based on thorax auscultation still represents an underexplored field in the study of rheumatoid arthritis.In this study, we investigate the problem of the automatic detection of velcro crackle in lung sounds. In practice, the patient is auscultated using a digital stethoscope and the lung sounds are saved to a file. The acquired digital data are then analysed using a suitably developed algorithm. In particular, the proposed solution relies on the empirical observation that the audio bandwidth associated with velcro crackle is larger than that associated with healthy breath sounds. Experimental results from a database of 70 patients affected by rheumatoid arthritis demonstrate that the developed tool can outperform specialized physicians in terms of diagnosing pulmonary disorders. The overall accuracy of the proposed solution is 90.0%, with negative and positive predictive values of 95.0% and 83.3%, respectively, whereas the reliability of physician diagnosis is in the range of 60−70%. The devised algorithm represents an enabling technology for a novel approach to the diagnosis of interstitial lung diseases in patients affected by rheumatoid arthritis.

Multilevel Delta Modulation with Switched First-Order Prediction for Wideband Speech Coding

In this paper a delta modulation speech coding scheme based on the ITU-T G.711 standard and the switched first-order predictor is presented. The forward adaptive scheme is used, where the adaptation to the signal variance is performed on frame-by-frame basis. The classification of the frames into weakly and highly correlated was done based on the correlation coefficient calculated for each frame, providing a basis for choosing the appropriate predictor coefficient. The obtained results indicate that the proposed model significantly outperforms the scalar companding system based on the G.711 standard. The obtained experimental results were verified using the theoretical model in the wide dynamic range of the input variance. DOI: http://dx.doi.org/10.5755/j01.eie.24.1.20156

Nonlinear prediction with deep recurrent neural networks for non-blind audio bandwidth extension

Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods.

高能率符号励振線形予測符号化と帯域選択型変形離散余弦変換符号化を用いた32kbit/sスケーラブル広帯域音声・オーディオ符号化方式の開発

高能率符号励振線形予測符号化と帯域選択型変形離散余弦変換符号化を用いた32kbit/sスケーラブル広帯域音声・オーディオ符号化方式の開発